基于长短期记忆网络的常见钻井工况识别方法研究

Study on the Identification of Common Drilling Operating Conditions Based on a Long Short-Term Memory Network

摘要: 为解决基于阈值设定和专家经验的钻井工况识别方法在时序建模与分类精度的局限，本文提出了一种基于长短期记忆网络的钻井工况识别方法。该方法通过门控机制有效捕捉钻井过程中时序数据的长期依赖关系，能够自动学习不同工况在时间维度上的变化趋势，并通过堆叠多层LSTM单元增强特征提取能力，并在LSTM输出端接入前馈全连接神经网络进行工况分类，以学习最优模型参数。选用川渝地区10口已完钻的水平井的实时钻井数据，对ANN、RNN、CNN、LSTM模型进行了性能测试，结果表明：LSTM模型在所有评估指标上均表现出最优异的性能，其精确率达到0.94，精度和召回率均为0.95，最终得到高达0.96的F1分数，这充分说明了LSTM模型能够有效捕捉这些复杂的序列特征，能够有效学习并记忆关键信息，从而实现最准确的分类预测。该方法为钻井工况的精准识别提供了新的技术路线，并对推动钻井监督智能化升级具有重要意义。

Abstract: To address the limitations of drilling operating condition identification methods based on threshold settings and expert experience in terms of time-series modeling and classification accuracy, this study proposes a drilling operating condition identification method based on a long short-term memory (LSTM) network. Through its gating mechanism, the proposed method effectively captures the long-term dependencies in time-series drilling data and automatically learns the temporal variation patterns associated with different operating conditions. Multiple LSTM layers are stacked to enhance feature extraction, and a feedforward fully connected neural network is connected to the output of the LSTM network for operating condition classification and optimal model parameter learning. Real-time drilling data from 10 completed horizontal wells in the Sichuan-Chongqing region were selected to evaluate the performance of ANN, RNN, CNN, and LSTM models. The results show that the LSTM model achieves the best performance across all evaluation metrics, with a precision of 0.94, an accuracy and recall of 0.95, and an F1-score of 0.96. These results demonstrate that the LSTM model can effectively capture complex sequential features and learn and retain critical information, thereby achieving highly accurate classification. The proposed method provides a new technical approach for the accurate identification of drilling operating conditions and is of great significance for promoting the intelligent upgrading of drilling supervision.